Topic: perceptual decision making

Cognitive neuroscience studies routinely concentrate on calculating the correlation coefficient between trial-averaged Event-Related Potentials (ERPs) and behavioral performance such as response time and accuracy. However there are some disadvantages in this traditional approach: 1) ignoring the variance of EEG data across trials, 2) requiring a large number of participants to find robust inferences, and 3) a lack of formal cognitive models to explain cognition. In this work, we used the drift-diffusion model to decompose perceptual decision making to underlying latent variables to explain be

Convolutional neural networks currently provide the best models of biological vision. However, their decision behavior, including the facts that they are deterministic and use equal number of computations for easy and difficult stimuli, differs markedly from human decision-making, thus limiting their applicability as models of human perceptual behavior. Here we develop a new neural network, RTNet, that generates stochastic decisions and human-like response time (RT) distributions, and also reproduces all foundational features of human accuracy, RT, and confidence. To test RTNet9s ability to pr

Recent studies have proposed that neural circuits have a $\textit{task manifold}$: i.e., a subset of the neural state-space to which neural activity is confined as an animal performs a task [1]. Thus, discovering and characterising these manifolds and their associated dynamics from experimental data can shed light on the neural computations unfolding within the brain during various cognitive tasks. Yet, common manifold discovery methods often do not take into account that neural data is generated by an underlying dynamical system. To address this, we first derive a general class of manifolds t

Perception requires integrating noisy dynamic visual information across the visual field to identify relevant stimuli and guide decisions. While temporal integration has been studied extensively in experiments with highly controlled visual stimuli and reverse-correlation techniques, the nonlinear mechanisms underlying spatial integration are often neglected. More concretely, neurons in the Middle Temporal area (MT) respond heterogeneously and nonlinearly to stimuli inside their receptive fields (RFs) [1-3], and these neurophysiological properties could mediate spatial suppression of motion obs